





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["pixel_values"]



	def __init__( self		,   a_ = True		,   a_ = None		,   a_ = None		,   a_ = PILImageResampling.BILINEAR		,   a_ = True		,   a_ = 1 / 255		,   a_ = True		,   a_ = None		,   a_ = None		,   **a_		,   ):
			super().__init__(**a_						)
			lowerCAmelCase  :     Optional[int]       						= size if size is not None else {"shortest_edge": 384}
			lowerCAmelCase  :     Dict       						= get_size_dict(a_		,   default_to_square=a_						)

			lowerCAmelCase  :     Any       						= do_resize
			lowerCAmelCase  :     List[str]       						= size
			# Default value set here for backwards compatibility where the value in config is None
			lowerCAmelCase  :     Any       						= crop_pct if crop_pct is not None else 224 / 256
			lowerCAmelCase  :     Dict       						= resample
			lowerCAmelCase  :     Optional[int]       						= do_rescale
			lowerCAmelCase  :     Union[str, Any]       						= rescale_factor
			lowerCAmelCase  :     Dict       						= do_normalize
			lowerCAmelCase  :     Dict       						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCAmelCase  :     Optional[int]       						= image_std if image_std is not None else IMAGENET_STANDARD_STD



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = PILImageResampling.BICUBIC		,   a_ = None		,   **a_		,   ):
			lowerCAmelCase  :     Optional[Any]       						= get_size_dict(a_		,   default_to_square=a_						)
			if "shortest_edge" not in size:
					raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''						)
			lowerCAmelCase  :     Optional[Any]       						= size["shortest_edge"]

			if shortest_edge < 384:
					# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
					lowerCAmelCase  :     List[Any]       						= int(shortest_edge / crop_pct						)
					lowerCAmelCase  :     Union[str, Any]       						= get_resize_output_image_size(a_		,   size=a_		,   default_to_square=a_						)
					lowerCAmelCase  :     List[str]       						= resize(image=a_		,   size=a_		,   resample=a_		,   data_format=a_		,   **a_						)
					# then crop to (shortest_edge, shortest_edge)
					return center_crop(image=a_		,   size=(shortest_edge, shortest_edge)		,   data_format=a_		,   **a_						)
			else:
					# warping (no cropping) when evaluated at 384 or larger
					return resize(
					    a_		,   size=(shortest_edge, shortest_edge)		,   resample=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return rescale(a_		,   scale=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return normalize(a_		,   mean=a_		,   std=a_		,   data_format=a_		,   **a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = ChannelDimension.FIRST		,   **a_		,   ):
			lowerCAmelCase  :     Optional[Any]       						= do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase  :     List[str]       						= crop_pct if crop_pct is not None else self.crop_pct
			lowerCAmelCase  :     str       						= resample if resample is not None else self.resample
			lowerCAmelCase  :     Optional[Any]       						= do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase  :     List[Any]       						= rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase  :     Tuple       						= do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase  :     Any       						= image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase  :     Tuple       						= image_std if image_std is not None else self.image_std

			lowerCAmelCase  :     Union[str, Any]       						= size if size is not None else self.size
			lowerCAmelCase  :     str       						= get_size_dict(a_		,   default_to_square=a_						)

			lowerCAmelCase  :     Optional[Any]       						= make_list_of_images(a_						)

			if not valid_images(a_						):
					raise ValueError(
					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
					    "torch.Tensor, tf.Tensor or jax.ndarray."						)

			if do_resize and size is None or resample is None:
					raise ValueError("Size and resample must be specified if do_resize is True."						)

			if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
					raise ValueError("crop_pct must be specified if size < 384."						)

			if do_rescale and rescale_factor is None:
					raise ValueError("Rescale factor must be specified if do_rescale is True."						)

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError("Image mean and std must be specified if do_normalize is True."						)

			# All transformations expect numpy arrays.
			lowerCAmelCase  :     int       						= [to_numpy_array(a_						) for image in images]

			if do_resize:
					lowerCAmelCase  :     Optional[Any]       						= [self.resize(image=a_		,   size=a_		,   crop_pct=a_		,   resample=a_						) for image in images]

			if do_rescale:
					lowerCAmelCase  :     Tuple       						= [self.rescale(image=a_		,   scale=a_						) for image in images]

			if do_normalize:
					lowerCAmelCase  :     str       						= [self.normalize(image=a_		,   mean=a_		,   std=a_						) for image in images]

			lowerCAmelCase  :     Any       						= [to_channel_dimension_format(a_		,   a_						) for image in images]

			lowerCAmelCase  :     List[str]       						= {"pixel_values": images}
			return BatchFeature(data=a_		,   tensor_type=a_						)










'''simple docstring'''






from __future__ import annotations






def   __A    (				a_    :	float   ,a_    :	float   ,a_    :	float   ,):
		if (electron_conc, hole_conc, intrinsic_conc).count(0						) != 1:
				raise ValueError("You cannot supply more or less than 2 values"						)
		elif electron_conc < 0:
				raise ValueError("Electron concentration cannot be negative in a semiconductor"						)
		elif hole_conc < 0:
				raise ValueError("Hole concentration cannot be negative in a semiconductor"						)
		elif intrinsic_conc < 0:
				raise ValueError(
				    "Intrinsic concentration cannot be negative in a semiconductor"						)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    intrinsic_conc**2 / hole_conc,
				)
		elif hole_conc == 0:
				return (
				    "hole_conc",
				    intrinsic_conc**2 / electron_conc,
				)
		elif intrinsic_conc == 0:
				return (
				    "intrinsic_conc",
				    (electron_conc * hole_conc) ** 0.5,
				)
		else:
				return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



